wg1 overview [email protected] deutscher wetterdienst, d-63067 offenbach, germany

COSMO General Meeting, Cracow, 15 – 19 Sept. 2008Overview on Data Assimilation [email protected]

WG1 Overview

WG1 Overview

[email protected] Deutscher Wetterdienst, D-63067 Offenbach, Germany

current DA method: nudgingto be developed: PP KENDA

for km-scale EPS

PP Sat-Cloud: • use of AMSU-A over land• use of cloud info from IR-rad

radar reflectivity (precip): • latent heat nudging• 1DVar

radar radial wind: • for nudging: VAD, SAR, nudg. Vr

ground-based GPS humidity • tomography (profiles)• vertically integrated WV

scatterometer10-m wind

improved use of surface-level obs

LETKFfor HRM

var. soil moisture analysis

snow (cover) PP COLOBOC


WG1 Overview

• DWD: Done: – microphysics change (2006) reduced evaporation below cloud base

ratio RRsurf / RRupper-air and hence RRsurf / RRref increased

(need to) revise definition of reference precipitation reduced overestimation of precipitation during LHN very small impact on forecasts

– bright band detection inside COSMO model

Outlook: – extend use of radar data to foreign radars– revise reference precipitation to account for (min.) radar beam height– better understand how (nature/) model develops convection

(role of environment, (moisture) balance, …)

Use of Radar-derived Surface Precipitation:

Latent Heat NudgingKlaus Stephan (DWD), Daniel Leuenberger (MetCH)

Talk: On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF

• MetCH: LHN introduced operationally 20 June 2008, extensive verification done


WG1 Overview

Bright Band detection (inside COSMO model)

Quelle: wikipedia

H_zero: height of freezing level in the modelH_radar: height of radar beamRR_RAD: hourly sum of precip. observed by radar

H_zero

H_radarBright Band criteria:

1. H_zero – H_radar [-300;600] RR_RAD(i,j)

2. > 8.5 <RR_RAD>


WG1 Overview Synop-Regnie Radar g.pts. with BB (≥1x/day)

ASS, LHN, no BB detect. ASS with BB detection ASS without LHN


WG1 Overview

Use of Radar-derived Surface PrecipitationVirginia Poli (ARPA-SMR)

Poster: Assimilation of radar derived surface rain rate into the regional COSMO model through a 1D-Var+nudging scheme: analysis of results

• ARPA-SMR: 1DVAR to retrieve T, q –profiles from RR (using linearised parameterisations of large-scale condensation and convection) then nudge T, q –profiles

Model space

Observation space

no

yes

Analysisxa=x

MINIMIZATION?0 Jx

bxJ

0J

Backgroundxb=(Tb, qb, ps

b)

x=(T, q, ps)

Initializationx=xb

0JRR

Jx

0Jx

bJ


WG1 Overview

Example of RR assimilation

Very encouraging results!

Shades: Radar observationContours: COSMO-I2 forecasted RR

Control run – Forecast +6 hours

Control run – Forecast +1 hour Experimental run – Forecast +1 hour

Experimental run – Forecast +6 hours

Assimilation of RR is able to dry off precipitation and also to create structures in the right place


WG1 Overview

Simple Adjoint Retrieval (SAR) of 3-D Wind VectorJerzy Achimowicz (IMGW) (W.P.1.1.2)

• input data: 3 consecutive scans of 3-d reflectivity and radial velocity at 10’-intervals, interpolated to Cartesian grid (1km x 1km x 500m , 20 levels)

smoothcontm JJJVVWWJ Bob

rrt

rob

tm mmm

2

,212

,21)(

xx

v

‘predicted’ by2100 ,, tob

tt mvvhhmt Fkk 22v

• SAR method: very sensitive to errors in radial velocity input data new software package developed for QC of radar Doppler data

(incl. de-aliasing, interpolation from polar to cartesian coord.)

Doppler radial velocity

wind retrieval


WG1 Overview

IWV derived from observed TZD (with p, T from Synop or COSMO)

Use of Integrated Water Vapour (IWV) from Ground-Based GPSMariella Tomassini, Klaus Stephan, Christoph Schraff (DWD) (W.P. 1.2)

q model

q gps

IWV gps < IWV mod

kqIWV

IWVkq v

obsobsv

modmod

pseudo-obs profile of specific humidity

kw

kpkqkw

satv

max

‘quality weights’ for ( ~ 1 betw. 700 – 800 hPa) : kqobs

v

• IWV from 169 Sta. every 15 min.(verify well with RS92-humidity, except for 12-UTC dry bias of RS92 in summer)

• 1 – 13 June 2007, anticyclonic air-mass convection

• 21-h forecasts from 0, 6, 12, 18 UTC ass cycle

• comparison: ‘CNT’ : like opr (with RS + LHN)‘GPS’ : CNT + GPS‘noRSq : CNT – RS-humidity

• GPS assimilated like radiosonde humidity profiles, but with smaller horizontal influence ( ~120 km → ~ 50 km)

Experiment


WG1 Overview

12 UTC

Analysis00 UTC

06 UTC

Obs18 UTC

CNT NoRSq

GPS

daily cycle of: IWV

CNT 00

GPS 00

CNT 12GPS 12

COSMO-DE too moist

12-UTC RS dries

GPS dries except at 12-UTC


WG1 Overview

RS verification : BIAS (model - obs)

+ 0 h

+ 6 h

CNT GPS NoRSq

00-UTC runs

+ 0 h

+ 6 h

12-UTC runs


WG1 Overview

Synop verification

00 UTC Forecast

06 UTC Forecast

12 UTC Forecast

18 UTC Forecast

Correct Cloud Cover Percent : GPS oooo CNT ****


WG1 Overview

hourly mean of precipitation (forecasts compared to radar)

+ + + + +

Obs

CNT

GPS

NoRSq

0.1 mm/h 0.1 mm/h

2.0 mm/h

00 UTC runs 12 UTC runs

2.0 mm/h

reduction of precip by GPS

increase of precip without RS-q


WG1 Overview

radar verification – ETS

0.1 mm/h 0.1 mm/h

1.0 mm/h 1.0 mm/h

00 UTC runs 12 UTC runs

+ + + + +

CNT

GPS

NoRSq

great improvement by GPS

GPS: worsebecause too little

strong precip in early evening


WG1 Overview

• GPS IWV obs from GFZ have good quality

further comparison / assimilation with GPS data from ~ 1000 European stations

(Eumetnet Project E-GVAP) main objects: data selection, extrapolation to 10 m, vertical + horizontal structure functions

• GPS data have shown 12-UTC dry bias of RS92 (in 2007) validate new version of RS92

• GPS data useful for verification of daily cycle of humidity in the model

test future development in data assimilation / physics with these data

• GPS IWV assimilation reduces overestimation of precip at night and has significant positive impact in first 8 hours of 0-UTC forecasts, but tends to suppress strong precip in afternoon

test again, when model physics improve daily cycle of precip, and test in winter

GPS – IWV : Conclusions & Outlook


WG1 Overview

Experiment 28 Feb – 9 March 2008 , with QuickScat & ASCAT datawith ASCAT / QuickScatno scatt

pmsl (model – obs)

too low

too strong

gradient

COSMO-EU

9-h forecasts,

valid for

6 March 2008,

9 UTC

Assimilation of Scatterometer 10-m WindHeinz-Werner Bitzer (MetBW), Alexander Cress, Christoph Schraff (DWD) (W.P. 1.5)

(10-m wind nudging with surface pressurecorrection which is in geostrophic balance with 10-m ana. incr.)


WG1 Overview

• aim: replace additional model runs by parameterized regressions to the determine the gradient of the cost function in the variational scheme(absolutely required for GME (long term dry drift), welcome for COSMO model)

Soil Moisture InitialisationMartin Lange, Werner Wergen (DWD) (W.P.1.8.1)

)()()()( 221

221 obs

mmTobs

mmbT

b TTOTTwwBwwJ

errorfcmT

bmobs

mT

mTmTT

mTbana wTTOBOww2

221

211

21

2 ))(()(

Cost function penalizes deviations from observations and initial soil moisture content

Analysed soil moisture depends on T2m forecast error and sensitivity T2m/w

0J

)00:0,(

)00:15,00:12(2

kw

T m

current scheme: by additional model runswith slightly different w(k,0:00)

new scheme: parameterised as a function of predicted latent heat flux at noon


WG1 Overview

Deutscher WetterdienstDeutscher WetterdienstDeutscher Wetterdienst

T2m (12 & 15 UTC) : good performance in summer , degredation in winter

Bias T2m on LM1 domain, avg 12:00, 15:00 RMSE T2m on LM1 domain, avg 12:00, 15:00

comparison of parameterised SMA with operational SMA:experiment May – November 2006

no SMA opr. SMAparam. SMA

no SMA opr. SMAparam. SMA


WG1 Overview

Deutscher WetterdienstDeutscher WetterdienstDeutscher Wetterdienst

Small change in top layers, higher wetness in bottom layers

soil moisture contentRMS of SMA increments, at layer 4 (9-27cm)( SMA incr. at layer 5 = 3 * (SMA incr. at layer 4) )

opr. SMA : top layerparam. SMA: top layer opr. SMA : bottom layerparam. SMA: bottom layer

opr. SMA param. SMA

• small differences in upper layers (until Nov.)• stronger moistening of lower layers

(further reduces positive T2m bias in summer)

comparison of parameterised SMA with operational SMA:experiment May – November 2006

parameterised SMA : almost zero increments during winter,

starting mid September


WG1 Overview

22

21

1

22 w

w

Tw

w

TT mm

m

nevaporatio

m

drainagenalgravitatio

mm

w

T

w

w

w

T

w

T

2

2

2

1

1

2

2

2

total differential:

sensitivity of T2m to w2

is different in operational and parameterised SMA in winter

parameterised(in winter: near zero

due to inactive plants)

not parameterised,but how does it look like

in the model (i.e. in the operational SMA)


WG1 Overview

8 cm

15 cm

60 cm

90 cm

soil water content: Lindenberg observations

15 cm: reacts after 6 hours

5 – 7 Nov 2006 (2 days)15 Oct 2006 – 1 Jan 2007 (2.5 months)

30 cm: reacts after 4 days

45 cm: reacts after 2 weeks

→ expect model layer 27 – 81 cm to take about 1 week to react

→ expect model layer 9 – 27 cm to take few hours at most to react


WG1 Overview

soil water content: model at Lindenberg

model layer 27 – 81 cm expectedto take about 1 week to react→ ok

model layer 9 – 27 cm expectedto take few hours at most to react→ ok

→ gravitational drainage (sedimentation) appears roughly realistic in COSMO→ soil moisture increments of operational SMA appear reasonable


WG1 Overview

parameterised SMA for COSMO: operational in 2009 (spring (?): simple version, autumn: with gravitational drainage)

drainagenalgravitatio

m

w

w

w

T

2

1

1

2

Outlook

parameterise also

can be derived analytically from Richards eq. used in COSMO (TERRA)

parameterisation already exists in current version of param. SMA

parameterised SMA for GME: full experiment started, operational in spring 2009

include RH2m as additional obs (param. implemented, increments reasonable in first case)

possible further extensions:• Analyse the top 5 soil layers separately instead of 2 aggregated layers (DWD).• Inclusion of precipitation analysis when good product is available (Suisse).• Improvement of model error statistics (Italy).

Note: SMA parameterisation needs some maintenance to account for future changes in the parameterisation of surface fluxes e.g. modification of root water uptake

cheap, efficient


WG1 Overview

Thank you for your attention


WG1 Overview

Advantages of NetCDF:• widely used and portable • a variety of software exists to plot, analyse and evaluate the data.

DWD plans: envisaged set-up observation formats, pre- and post-processingDWD plans: envisaged set-up observation formats, pre- and post-processing

can keep AOF asalternative data input

as long as needed

DWD switches to NetCDF on 17 Sept. 2008thereafter, DWD will no longer support AOF interface

~ 1 by 1 convertersimple + portableapplicable to WMO or non-WMO BUFR

standard WMO templates,i.e. unique descriptors + dimensions of elements + code tablesunique BUFR format for each obs type

NetCDF 2

ODBODB monitoring

NetCDFobs

3DVar NetCDFfeedbac

kCOSMOmodel

verificationNWPsection

any kind of

BUFR

bufr 2 wmo_bu

fr

WMOBUFR

bufr2netcdf

IT section

SKY /archive

Under discussion at DWD


WG1 Overview

analysis operational new T2m diagnostics

Deutscher Wetterdienst

COSMO-EU 20070427 00:00 +15 hours

New T2m diagnostics affects the whole PBL through SMA

Bias T2m, C-EU on LM1-domain, avg12:00, 15:00 Accumulated soil moisture increments

Rmse T2m, C-EU on LM1-domain, avg12:00, 15:00

10 m

2250 m

Dew point temperature Germany

both runs done with operational version

of SMA


WG1 Overview

mean skill scores over 32 forecast (00 and 12 UTC) AUGUST 2006threshold 0.1 mm/h

ETSFBI

ASS FORECAST ASS FORECAST

LHN and prognostic precipitation

shows impact of LHN refinements in 2005 / 06 (reference precip / LHN restricted to ‘cloudy layers’ / grid point search / limits)

Stephan, K., S. Klink, C. Schraff, 2008: Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Q. J. R. Meteorol. Soc., 134, 1315 – 1326.


WG1 Overview

New PP: Km-scale Ensemble-based Data Assimilation (KENDA)

Discussion with input from Chris Snyder 18 Sept 2007 on EnKFDiscussion with input from Chris Snyder 18 Sept 2007 on EnKF

– no new obstacles seen for the EnKF

– to get a system to evaluate, need 2 people (with good background) for 2 years

– do EnKF first without radar data (quality control problems), gain experiences, detect bugs / flaws in the scheme, later include radar data


WG1 Overview

Assimilation of Scatterometer Wind

29 Feb 08, 0 UTC

COSMO-EU ana with ASCAT/QuickScatCOSMO-EU ana , no scatt

ECMWF analysis 29 Feb 08ASCAT 28 Feb 08, 21 UTC ± 1.5h

984 hPamax. 30 kn

~15 m/s

10-m wind [m/s]


WG1 Overview

Variational assimilation

Model space

Observation space

no

yes

Analysisxa=x

MINIMIZATION?0 Jx

bxJ

0J

Backgroundxb=(Tb, qb, ps

b)

x=(T, q, ps)

Initializationx=xb

0JRR

Jx

0Jx

bJ

Convert observations (Rain Rates - RR) in profiles of temperature and humidity and nudge them as “pseudo”-observations.

wg1 overview [email protected] deutscher wetterdienst, d-63067 offenbach, germany

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